Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
32 - Quantifying Luck
from Part V - Applications
Published online by Cambridge University Press: 29 May 2026
By
Book contents
- The Cambridge Handbook of Behavioural Data Science
- Cambridge Handbooks in Psychology
- Reviews
- The Cambridge Handbook of Behavioural Data Science
- Copyright page
- Dedication
- Contents
- Contributors
- Preface
- Introduction
- Part I Introduction to Behavioural Data Science
- Part II Human Behaviour
- Part III Algorithmic Behaviour
- Part IV Systems and Culture
- Part V Applications
- 29 Applications of Behavioural Data Science
- 30 Pro-Social Nudging
- 31 Social Media Analytics
- 32 Quantifying Luck
- 33 Quantifying the Connection between Scenic Beauty and Reported Health Using Deep Learning and Econometrics
- 34 Money, Methodology and Happiness
- 35 Human–Data Interaction
- 36 Natural Language Processing in Behavioural Data Science
- 37 Understanding Collective Behaviour Using Online Data and Mobile Phones
- 38 Burstier Events
- 39 Behavioural Data Science in Financial Services
- 40 XR, VR and AR Applications in Behavioural Data Science
- 41 On Cryptoasset Traders’ Behaviour
- 42 Behavioural Data Science of Cybersecurity
- 43 Behavioural Data Science Ethics and Governance Pre-AI Act
- 44 Behavioural Data Science Ethics and Governance Post-AI Act
- Part VI Concluding Remarks
- Main Abbreviations and Acronyms
- Glossary
- References
Summary
Understanding the sources and consequences of luck has important behavioural and policy implications. Most prior research has treated luck as the residue of rationality, models or foresight. This chapter proposes a novel approach to help quantify the impact of luck and its interaction with human behaviours, particularly useful for behavioural data scientists. It illustrates the approach using three datasets with contexts that generate idiosyncratic patterns and behavioural implications, labelled swing luck, undeserved luck and network-bounded luck. The chapter concludes by discussing how this approach can be applied to other contexts and its scope conditions.
Information
- Type
- Chapter
- Information
- The Cambridge Handbook of Behavioural Data Science , pp. 565 - 582Publisher: Cambridge University PressPrint publication year: 2026
References
Arthur, W. B. (1989). Competing technologies, increasing returns, and lock-in by historical events. The Economic Journal, 99(394), 116–131.10.2307/2234208CrossRefGoogle Scholar
Barabasi, A. L. & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.10.1126/science.286.5439.509CrossRefGoogle ScholarPubMed
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.10.1177/014920639101700108CrossRefGoogle Scholar
Baum, J. A. C. (2011). Free-riding on power laws: Questioning the validity of the Impact Factor as a measure of research quality in organization studies. Organization, 18(4), 449–466.10.1177/1350508411403531CrossRefGoogle Scholar
Benner, M. J. & Zenger, T. (2016). The lemons problem in markets for strategy. Strategy Science, 1(2), 71–89.10.1287/stsc.2015.0010CrossRefGoogle Scholar
Bertrand, M. & Mullainathan, S. (2001). Are CEOs rewarded for luck? The ones without principals are. Quarterly Journal of Economics, 116(3), 901–932.10.1162/00335530152466269CrossRefGoogle Scholar
Brandenburger, A. & Polak, B. (1996). When managers cover their posteriors: Making the decisions the market wants to see. The RAND Journal of Economics, 27(3), 523–541.10.2307/2555842CrossRefGoogle Scholar
Cockburn, I. M., Henderson, R. M. & Stern, S. (2017). Untangling the origins of competitive advantage, in The Blackwell handbook of organizational capabilities: Emergence, development, and change, edited by Foss, N. J. & Robertson, D.. Wiley-Blackwell, 296–325.10.1002/9781405164054.ch19CrossRefGoogle Scholar
Correll, S. J., Ridgeway, C. L., Zuckerman, E. W. & Jank, S. (2017). It’s the conventional thought that counts: How third-order inference produces status advantage. American Sociological Review, 82(2), 297–327.10.1177/0003122417691503CrossRefGoogle Scholar
De Vany, A. (2004). Hollywood economics: How extreme uncertainty shapes the film industry. Routledge.Google Scholar
Denrell, J. (2004). Random walks and sustained competitive advantage. Management Science, 50(7), 922–934.10.1287/mnsc.1030.0143CrossRefGoogle Scholar
Denrell, J., Fang, C. & Liu, C. (2015). Chance explanations in the management sciences. Organization Science, 26(3), 923–940.10.1287/orsc.2014.0946CrossRefGoogle Scholar
Denrell, J., Fang, C. & Liu, C. (2019). In search of behavioral opportunities from misattributions of luck. Academy of Management Review, 44(4), 896–915.10.5465/amr.2017.0239CrossRefGoogle Scholar
Denrell, J. & Liu, C. (2012). Top performers are not the most impressive when extreme performance indicates unreliability. Proceedings of the National Academy of Sciences, 109(24), 9331–9336.10.1073/pnas.1116048109CrossRefGoogle Scholar
Denrell, J. & Liu, C. (2021). When reinforcing processes generate an outcome-quality dip. Organization Science, 32(4), 1079–1099.10.1287/orsc.2020.1414CrossRefGoogle Scholar
Denrell, J., Liu, C. & Le Mens, G. (2017). When more selection is worse. Strategy Science, 2(1), 39–63.10.1287/stsc.2017.0025CrossRefGoogle Scholar
Dillon, R. L. & Tinsley, C. H. (2008). How near-misses influence decision making under risk: A missed opportunity for learning. Management Science, 54(8), 1425–1440.10.1287/mnsc.1080.0869CrossRefGoogle Scholar
Elberse, A. (2013). Blockbusters: Hit-making, risk-taking, and the big business of entertainment. Macmillan.Google Scholar
Feldman, J. M. (1981). Beyond attribution theory: Cognitive processes in performance appraisal. Journal of Applied Psychology, 66(2), 127–148.10.1037/0021-9010.66.2.127CrossRefGoogle Scholar
Feller, W. (1968). An introduction to probability theory and its applications, Vol. II. Wiley.Google Scholar
Greve, H. R. (2003). Organizational learning from performance feedback: A behavioral perspective on innovation and change. Cambridge University Press.10.1017/CBO9780511615139CrossRefGoogle Scholar
Harrison, J. R. & March, J. G. (1984). Decision making and postdecision surprises. Administrative Science Quarterly, 29(1), 26–42.10.2307/2393078CrossRefGoogle Scholar
Hart, O. D. & Holmström, B. (1987). The theory of contracts, in Advances in economic theory: Fifth World Congress, edited by Bewley, T.. Cambridge University Press, 7–46.Google Scholar
Holmström, B. (2017). Pay for performance and beyond. American Economic Review, 107(7), 1753–1777.10.1257/aer.107.7.1753CrossRefGoogle Scholar
Kahneman, D., Slovic, P. & Tversky, A. (1982). Judgment under uncertainty: Heuristics and biases. Cambridge University Press.10.1017/CBO9780511809477CrossRefGoogle Scholar
Levinthal, D. A. (1991). Random walks and organizational mortality. Administrative Science Quarterly, 36(3), 397–420.10.2307/2393202CrossRefGoogle Scholar
Levitt, B. & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.10.1146/annurev.so.14.080188.001535CrossRefGoogle Scholar
Litov, L. P., Moreton, P. & Zenger, T. R. (2012). Corporate strategy, analyst coverage, and the uniqueness paradox. Management Science, 58(10), 1797–1815.10.1287/mnsc.1120.1530CrossRefGoogle Scholar
Liu, C. (2019). Luck: A key idea for business and society. Routledge.10.4324/9781315106144CrossRefGoogle Scholar
Liu, C. (2021). Why do firms fail to engage diversity? A behavioral strategy perspective. Organization Science, 32(5), 1193–1209.10.1287/orsc.2020.1425CrossRefGoogle Scholar
Liu, C. & de Rond, M. (2016). Good night and good luck: Perspectives on luck in management scholarship. Academy of Management Annals, 10(1), 409–451.10.5465/19416520.2016.1120971CrossRefGoogle Scholar
Liu, C. & Tsay, C. J. (2023). A normative theory of luck. Frontiers in Psychology, 14, 1157527.10.3389/fpsyg.2023.1157527CrossRefGoogle ScholarPubMed
Lynn, F. B., Podolny, J. M. & Tao, L. (2009). A sociological (de)construction of the relationship between status and quality. American Journal of Sociology, 115(3), 755–804.10.1086/603537CrossRefGoogle Scholar
March, J. C. & March, J. G. (1977). Almost random careers: The Wisconsin school superintendency, 1940–1972. Administrative Science Quarterly, 22(3), 377–409.10.2307/2392180CrossRefGoogle Scholar
March, J. G. (1991). Exploration and exploitation in organizational learning. Organization Science, 2(1), 71–87.10.1287/orsc.2.1.71CrossRefGoogle Scholar
Meindl, J. R., Ehrlich, S. B. & Dukerich, J. M. (1985). The romance of leadership. Administrative Science Quarterly, 30(1), 78–102.10.2307/2392813CrossRefGoogle Scholar
Merton, R. K. (1968). The Matthew Effect in science: The reward and communication systems of science. Science, 159(3810), 55–63.10.1126/science.159.3810.56CrossRefGoogle Scholar
Podsakoff, P. M., MacKenzie, S. B., Podsakoff, N. P. & Bachrach, D. G. (2008). Scholarly influence in the field of management: A bibliometric analysis of the determinants of university and author impact in the management literature in the past quarter century. Journal of Management, 34(4), 641–720.10.1177/0149206308319533CrossRefGoogle Scholar
Rahmandad, H. & Repenning, N. (2016). Capability erosion dynamics. Strategic Management Journal, 37(4), 649–672.10.1002/smj.2354CrossRefGoogle Scholar
Repenning, N. P. & Sterman, J. D. (2002). Capability traps and self-confirming attribution errors in the dynamics of process improvement. Administrative Science Quarterly, 47(2), 265–295.10.2307/3094806CrossRefGoogle Scholar
Rudolph, J.W. & Repenning, N. P. (2002). Disaster dynamics: Understanding the role of quantity in organizational collapse. Administrative Science Quarterly, 47(1), 1–30.10.2307/3094889CrossRefGoogle Scholar
Salganik, M. J., Dodds, P. S. & Watts, D. J. (2006). Experimental study of inequality and unpredictability in an artificial cultural market. Science, 311(5762), 854–856.10.1126/science.1121066CrossRefGoogle Scholar
Samuels, M. L. (1991). Statistical reversion toward the mean: More universal than regression toward the mean. The American Statistician, 45(4), 344–346.10.1080/00031305.1991.10475833CrossRefGoogle Scholar
Schmittlein, D. C. (1989). Surprising inferences from unsurprising observations: Do conditional expectations really regress to the mean? The American Statistician, 43(3), 176–183.10.1080/00031305.1989.10475651CrossRefGoogle Scholar
Schwab, A., Abrahamson, E., Starbuck, W. H. & Fidler, F. (2011). Perspective: Researchers should make thoughtful assessments instead of null-hypothesis significance tests. Organization Science, 22(4), 1105–1120.10.1287/orsc.1100.0557CrossRefGoogle Scholar
Starbuck, W. H. (1994). On behalf of naivete, in Evolutionary dynamics of organizations, edited by Baum, J & Singh, J. Oxford University Press, 205–220.10.1093/oso/9780195077360.003.0014CrossRefGoogle Scholar
Taleb, N. N. (2001). Fooled by randomness: The hidden role of chance in life and in the markets. Penguin.Google Scholar
Accessibility standard: WCAG 2.2 AAA
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
The HTML of this chapter
complies with version 2.2 of the
Web Content Accessibility Guidelines
(WCAG), offering more comprehensive accessibility measures for a broad range
of users and attains the highest
(AAA)
level of WCAG compliance, optimising the user experience by meeting the most extensive
accessibility guidelines.
Content Navigation
Table of contents navigation
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Allows you to navigate directly to chapters, sections, or non‐text items through a linked table of contents, reducing the need for extensive scrolling.
Index navigation
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Provides an interactive index, letting you go straight to where a term or subject appears in the text without manual searching.
Reading Order & Textual Equivalents
Single logical reading order
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
You will encounter all content (including footnotes, captions, etc.) in a clear, sequential flow, making it easier to follow with assistive tools like screen readers.
Short alternative textual descriptions
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
You get concise descriptions (for images, charts, or media clips), ensuring you do not miss crucial information when visual or audio elements are not accessible.
Full alternative textual descriptions
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
You get more than just short alt text: you have comprehensive text equivalents, transcripts, captions, or audio descriptions for substantial non‐text content, which is especially helpful for complex visuals or multimedia.
Visualised data also available as non-graphical data
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
You can access graphs or charts in a text or tabular format, so you are not excluded if you cannot process visual displays.
Visual Accessibility
Use of colour is not sole means of conveying information
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
You will still understand key ideas or prompts without relying solely on colour, which is especially helpful if you have colour vision deficiencies.
Use of high contrast between text and background colour
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.
You benefit from high‐contrast text, which improves legibility if you have low vision or if you are reading in less‐than‐ideal lighting conditions.